Proceedings ArticleDOI
Data clustering using particle swarm optimization
D. Van der Merwe,Andries P. Engelbrecht +1 more
- Vol. 1, pp 215-220
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This paper proposes two new approaches to using PSO to cluster data, one which basically usesPSO to refine the clusters formed by K-means, and the other which uses PSO in a different way to seed the initial swarm.Abstract:
This paper proposes two new approaches to using PSO to cluster data. It is shown how PSO can be used to find the centroids of a user specified number of clusters. The algorithm is then extended to use K-means clustering to seed the initial swarm. This second algorithm basically uses PSO to refine the clusters formed by K-means. The new PSO algorithms are evaluated on six data sets, and compared to the performance of K-means clustering. Results show that both PSO clustering techniques have much potential.read more
Citations
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Journal ArticleDOI
A Comprehensive Survey of Clustering Algorithms
Dongkuan Xu,Yingjie Tian +1 more
TL;DR: This review paper begins at the definition of clustering, takes the basic elements involved in the clustering process, such as the distance or similarity measurement and evaluation indicators, into consideration, and analyzes the clustered algorithms from two perspectives, the traditional ones and the modern ones.
Journal ArticleDOI
A survey on nature inspired metaheuristic algorithms for partitional clustering
TL;DR: An up-to-date review of all major nature inspired metaheuristic algorithms employed till date for partitional clustering and key issues involved during formulation of various metaheuristics as a clustering problem and major application areas are discussed.
Proceedings ArticleDOI
Document clustering using particle swarm optimization
TL;DR: This paper presents a particle swarm optimization (PSO) document clustering algorithm, which performs a globalized search in the entire solution space and shows that the hybrid PSO algorithm can generate more compact clustering results than the K-means algorithm.
BookDOI
Music-Inspired Harmony Search Algorithm
TL;DR: This first chapter intends to review and analyze the powerful new Harmony Search algorithm in the context of metaheuristic algorithms, and tries to identify the characteristics of meta heuristics and analyze why HS is a good meta heuristic algorithm.
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Clustering Algorithms in Biomedical Research: A Review
Rui Xu,Donald C. Wunsch +1 more
TL;DR: This paper is presented to provide biomedical researchers with an overview of the status quo of clustering algorithms, to illustrate examples of biomedical applications based on cluster analysis, and to help biomedical researchers select the most suitable clustering algorithm for their own applications.
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Book
C4.5: Programs for Machine Learning
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
Book
Remote sensing and image interpretation
TL;DR: In this article, the authors present a textbook for introductory courses in remote sensing, which includes concepts and foundations of remote sensing; elements of photographic systems; introduction to airphoto interpretation; air photo interpretation for terrain evaluation; photogrammetry; radiometric characteristics of aerial photographs; aerial thermography; multispectral scanning and spectral pattern recognition; microwave sensing; and remote sensing from space.